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What is the difference between deep reinforcement learning and reinforcement learning? I basically know what reinforcement learning is about, but what does the concrete term deep stand for in this context?
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The difference between them is that deep learning is learning from a training set and then applying that learning to a new data set, while reinforcement learning is dynamically learning by adjusting actions based in continuous feedback to maximize a reward.
Reinforcement Learning (RL) is the science of decision making. It is about learning the optimal behavior in an environment to obtain maximum reward.
Deep learning algorithms perform much better, by giving better accuracy, than machine learning algorithms when there is a lot of data available for them to learn from. However, these algorithms will be computationally expensive and require the use of a GPU to make use of them.
Deep reinforcement learning (deep RL) is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the problem of a computational agent learning to make decisions by trial and error.
In reinforcement learning, an agent tries to come up with the best action given a state.
For example, in the video game Pac-Man, the state space would be the 2D game world you are in, the surrounding items (pac-dots, enemies, walls, etc), and actions would be moving through that 2D space (going up/down/left/right).
So, given the state of the game world, the agent needs to pick the best action to maximise rewards. Through reinforcement learning's trial and error, it accumulates "knowledge" through these (state, action) pairs, as in, it can tell if there would be positive or negative reward given a (state, action) pair. Let's call this value Q(state, action).
A rudimentary way to store this knowledge would be a table like below
state | action | Q(state, action) --------------------------------- ... | ... | ... The (state, action) space can be very big
However, when the game gets complicated, the knowledge space can become huge and it no longer becomes feasible to store all (state, action) pairs. If you think about it in raw terms, even a slightly different state is still a distinct state (e.g. different position of the enemy coming through the same corridor). You could use something that can generalize the knowledge instead of storing and looking up every little distinct state.
So, what you can do is create a neural network, that e.g. predicts the reward for an input (state, action) (or pick the best action given a state, however you like to look at it)
Approximating the Q value with a Neural Network
So, what you effectively have is a NN that predicts the Q value, based on the input (state, action). This is way more tractable than storing every possible value like we did in the table above.
Q = neural_network.predict(state, action) Deep Neural Networks
To be able to do that for complicated games, the NN may need to be "deep", meaning a few hidden layers may not suffice to capture all the intricate details of that knowledge, hence the use of deep NNs (lots of hidden layers).
The extra hidden layers allows the network to internally come up with features that can help it learn and generalize complex problems that may have been impossible on a shallow network.
In short, the deep neural network allows reinforcement learning to be applied to larger problems. You can use any function approximator instead of an NN to approximate Q, and if you do choose NNs, it doesn't absolutely have to be a deep one. It's just researchers have had great success using them recently.
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